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    Item Transfer Control Systems
    4.
    发明公开
    Item Transfer Control Systems 审中-公开

    公开(公告)号：US20230206171A1

    公开(公告)日：2023-06-29

    申请号：US18117586

    申请日：2023-03-06

    Applicant: Adobe Inc.

    Inventor： Kirankumar Shiragur ,  Tung Thanh Mai ,  Anup Bandigadi Rao ,  Ryan A. Rossi ,  Georgios Theocharous ,  Michele Saad

    IPC: G06Q10/0835 ,  G06F17/11 ,  G06Q10/087 ,  G06Q10/047

    CPC classification number: G06Q10/08355 ,  G06F17/11 ,  G06Q10/087 ,  G06Q10/047

    Abstract: In implementations of item transfer control systems, a computing device implements a transfer system to receive input data describing types of requested items and corresponding quantities of the types of requested items to receive at each of a plurality of destination sites and types of available items and corresponding quantities of the types of available items that are available at each of a plurality of source sites. The transfer system constructs a flow network having a source node for each of the plurality of the source sites and a destination node for each of the plurality of the destination sites. An integral approximate solution is generated that transfers the corresponding quantities of the types of requested items to each of the plurality of the destination sites using a maximum flow solver and the flow network. The transfer system causes transferences of the corresponding quantities of the types of requested items to each of the plurality of the destination sites based on the integral approximate solution.

    Item transfer control systems
    5.
    发明授权
    Item transfer control systems 有权

    公开(公告)号：US11636423B2

    公开(公告)日：2023-04-25

    申请号：US17394707

    申请日：2021-08-05

    Applicant: Adobe Inc.

    Inventor： Kirankumar Shiragur ,  Tung Thanh Mai ,  Anup Bandigadi Rao ,  Ryan A. Rossi ,  Georgios Theocharous ,  Michele Saad

    IPC: G06Q10/0835 ,  G06F17/11 ,  G06Q10/087 ,  G06Q10/047

    Abstract: In implementations of item transfer control systems, a computing device implements a transfer system to receive input data describing types of requested items and corresponding quantities of the types of requested items to receive at each of a plurality of destination sites and types of available items and corresponding quantities of the types of available items that are available at each of a plurality of source sites. The transfer system constructs a flow network having a source node for each of the plurality of the source sites and a destination node for each of the plurality of the destination sites. An integral approximate solution is generated that transfers the corresponding quantities of the types of requested items to each of the plurality of the destination sites using a maximum flow solver and the flow network. The transfer system causes transferences of the corresponding quantities of the types of requested items to each of the plurality of the destination sites based on the integral approximate solution.

    Deep Hybrid Graph-Based Forecasting Systems
    6.
    发明申请
    Deep Hybrid Graph-Based Forecasting Systems 有权

    公开(公告)号：US20220138557A1

    公开(公告)日：2022-05-05

    申请号：US17089157

    申请日：2020-11-04

    Applicant: Adobe Inc.

    Inventor： Ryan A. Rossi ,  Hongjie Chen ,  Kanak Vivek Mahadik ,  Sungchul Kim

    IPC: G06N3/08 ,  G06N3/04 ,  G06F16/2458

    Abstract: In implementations of deep hybrid graph-based forecasting systems, a computing device implements a forecast system to receive time-series data describing historic computing metric values for a plurality of processing devices. The forecast system determines dependency relationships between processing devices of the plurality of processing devices based on time-series data of the processing devices. Time-series data of each processing device is represented as a node of a graph and the nodes are connected based on the dependency relationships. The forecast system generates an indication of a future computing metric value for a particular processing device by processing a first set of the time-series data using a relational global model and processing a second set of the time-series data using a relational local model. The first and second sets of the time-series data are determined based on a structure of the graph.

    SYSTEM AND METHOD FOR RESOURCE SCALING FOR EFFICIENT RESOURCE MANAGEMENT
    7.
    发明申请
    SYSTEM AND METHOD FOR RESOURCE SCALING FOR EFFICIENT RESOURCE MANAGEMENT 有权

    公开(公告)号：US20210357255A1

    公开(公告)日：2021-11-18

    申请号：US16867104

    申请日：2020-05-05

    Applicant: ADOBE INC.

    Inventor： Kanak Vivek Mahadik ,  Ryan A. Rossi ,  Sana Malik Lee ,  Georgios Theocharous ,  Handong Zhao ,  Gang Wu ,  Youngsuk Park

    IPC: G06F9/50 ,  G06F11/34 ,  G06F11/30 ,  G06F17/10

    Abstract: A system and method for automatically adjusting computing resources provisioned for a computer service or application by applying historical resource usage data to a predictive model to generate predictive resource usage. The predictive resource usage is then simulated for various service configurations, determining scaling requirements and resource wastage for each configuration. A cost value is generated based on the scaling requirement and resource wastage, with the cost value for each service configuration used to automatically select a configuration to apply to the service. Alternatively, the method for automatically adjusting computer resources provisioned for a service may include receiving resource usage data of the service, applying it to a linear quadratic regulator (LQR) to find an optimal stationary policy (treating the resource usage data as states and resource-provisioning variables as actions), and providing instructions for configuring the service based on the optimal stationary policy.

    Higher-order graph clustering
    8.
    发明授权
    Higher-order graph clustering 有权

    公开(公告)号：US11163803B2

    公开(公告)日：2021-11-02

    申请号：US16397839

    申请日：2019-04-29

    Applicant: Adobe Inc.

    Inventor： Ryan A. Rossi ,  Eunyee Koh ,  Anup Bandigadi Rao ,  Aldo Gael Carranza

    IPC: G06F16/28 ,  G06F16/22

    Abstract: In implementations of higher-order graph clustering and embedding, a computing device receives a heterogeneous graph representing a network. The heterogeneous graph includes nodes that each represent a network entity and edges that each represent an association between two of the nodes in the heterogeneous graph. To preserve node-type and edge-type information, a typed graphlet is implemented to capture a connectivity pattern and the types of the nodes and edges. The computing device determines a frequency of the typed graphlet in the graph and derives a weighted typed graphlet matrix to sort graph nodes. Sorted nodes are subsequently analyzed to identify node clusters having a minimum typed graphlet conductance score. The computing device is further implemented to determine a higher-order network embedding for each of the nodes in the graph using the typed graphlet matrix, which can then be concatenated into a matrix representation of the network.

    Multi-Item Influence Maximization
    9.
    发明申请
    Multi-Item Influence Maximization 有权

    公开(公告)号：US20210142425A1

    公开(公告)日：2021-05-13

    申请号：US16677007

    申请日：2019-11-07

    Applicant: Adobe Inc.

    Inventor： Ryan A. Rossi

    IPC: G06Q50/00 ,  G06Q30/02

    Abstract: In implementations of multi-item influence maximization, a computing device can obtain updates to a user association graph that indicates social correspondence between users, and obtain updates to a user-item graph that indicates user correspondence with one or more items. The computing device includes an influence maximization module that can update an item association graph that indicates item correspondence of each item with one or more other items, where the item association graph can be updated based on the user-item graph that indicates the user correspondence with one or more of the items. The influence maximization module can then iteratively determine a resource allocation for each of the users to maximize user influence of multiple items that are associated in the item association graph and based on the social correspondence between the users, as well as assign a variable portion of the resource allocation to any number of the users.

    FIGURE CAPTIONING SYSTEM AND RELATED METHODS
    10.
    发明申请
    FIGURE CAPTIONING SYSTEM AND RELATED METHODS 审中-公开

    公开(公告)号：US20200285951A1

    公开(公告)日：2020-09-10

    申请号：US16296076

    申请日：2019-03-07

    Applicant: ADOBE INC.

    Inventor： Sungchul Kim ,  Scott Cohen ,  Ryan A. Rossi ,  Charles Li Chen ,  Eunyee Koh

    IPC: G06N3/08 ,  G06N3/04 ,  G06K9/62 ,  G06F17/28

    Abstract: Embodiments of the present invention are generally directed to generating figure captions for electronic figures, generating a training dataset to train a set of neural networks for generating figure captions, and training a set of neural networks employable to generate figure captions. A set of neural networks is trained with a training dataset having electronic figures and corresponding captions. Sequence-level training with reinforced learning techniques are employed to train the set of neural networks configured in an encoder-decoder with attention configuration. Provided with an electronic figure, the set of neural networks can encode the electronic figure based on various aspects detected from the electronic figure, resulting in the generation of associated label map(s), feature map(s), and relation map(s). The trained set of neural networks employs a set of attention mechanisms that facilitate the generation of accurate and meaningful figure captions corresponding to visible aspects of the electronic figure.

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